Practical Agentic RAG patterns implemented with LangGraph

Developer Chandula7 released a free demo Jupyter notebook, 1_Agentic_RAG.ipynb, the first of four self-contained notebooks implementing distinct agentic Retrieval-Augmented Generation (RAG) patterns with LangGraph. The series covers Agentic RAG, Corrective RAG (CRAG), Adaptive RAG, and Human-in-the-Loop RAG, with the full set available via Gumroad and the notebooks requiring Python 3.11+, uv, a Groq API key, and a Tavily API key for the Corrective and Adaptive patterns.

Four self-contained Jupyter notebooks, each implementing a different way of making a Retrieval-Augmented Generation RAG pipeline "agentic" — able to decide, check itself, correct its own mistakes, or defer to a person, instead of blindly retrieving once and answering. | Notebook | Pattern | What makes it agentic | |---|---|---| | 1 Agentic RAG.ipynb https://chandula7.gumroad.com/l/AgenticRAGFundamentals free demo | Agentic RAG | An LLM agent decides whether to retrieve at all, and which of two knowledge bases to search, using tool calling. | | 2 Corrective RAG.ipynb https://chandula7.gumroad.com/l/Advanced RAG LangGraph Patterns | Corrective RAG CRAG | Always retrieves first, then grades what it got — and automatically falls back to a live web search if the local documents aren't good enough. | | 3 Adaptive RAG.ipynb https://chandula7.gumroad.com/l/Advanced RAG LangGraph Patterns | Adaptive RAG | Routes each question to a vectorstore or the web before retrieving, then grades both the documents and the final answer hallucination + relevance checks before returning it. | | 4 Human in the Loop RAG.ipynb https://chandula7.gumroad.com/l/Advanced RAG LangGraph Patterns | Human-in-the-Loop RAG | Takes the same retrieve/grade/generate machinery and replaces the automatic loop-decisions with a person: the graph pauses after retrieval and after generation, shows the LLM's grades as advisory suggestions only, and waits for a human to approve, request a revision, or send it back. | Each notebook is fully commented with markdown cells explaining what every step does and why — you don't need to already know LangGraph to follow along. This is the free demo notebook 1 Agentic RAG.ipynb . The full series — including Corrective RAG, Adaptive RAG, and Human-in-the-Loop RAG — is available here: Agentic RAG — Four Working Patterns with LangGraph https://chandula7.gumroad.com/l/Advanced RAG LangGraph Patterns They sit on a spectrum of how much a RAG pipeline second-guesses itself — and who gets the final say: - Agentic RAG — the retrieval decision itself is delegated to the LLM. - Corrective RAG — retrieval always happens, but the result is checked and corrected with a web-search fallback. - Adaptive RAG — adds routing at the front vectorstore vs. web and a second self-check at the very end, on the generated answer itself. - Human-in-the-Loop RAG — keeps the same grading/self-check machinery as Adaptive RAG, but the LLM's verdicts stop being routing decisions and become suggestions: a real person approves, revises, or rejects at two checkpoints using LangGraph's interrupt / Command resume=... pattern with a MemorySaver checkpointer. Understanding all four, and where each one is worth the extra complexity, is more useful than knowing just one. - VS Code https://code.visualstudio.com/ with the Python and Jupyter extensions - Python 3.11+ - uv https://docs.astral.sh/uv/ — a fast, single-binary Python package/environment manager. It replaces pip + venv with one tool and one lockfile. - A Groq https://console.groq.com/keys API key free tier available — used by all four notebooks as the LLM. - A Tavily https://app.tavily.com API key free tier available — used by the Corrective RAG and Adaptive RAG notebooks for live web search. Not needed for Agentic RAG or Human-in-the-Loop RAG. Pick whichever matches your setup — you only need one of these. macOS / Linux: curl -LsSf https://astral.sh/uv/install.sh | sh Windows PowerShell : powershell -c "irm https://astral.sh/uv/install.ps1 | iex" Already have Python + pip and prefer not to run an install script: pip install uv Other options Homebrew, pipx , winget , cargo , standalone downloads are listed in the official install docs https://docs.astral.sh/uv/getting-started/installation/ . Check it worked: uv --version From the project folder: Creates a .venv and installs every dependency from pyproject.toml, pinned exactly via uv.lock, in one step. uv sync Register the environment as a Jupyter kernel. uv run python -m ipykernel install --user --name agentic-rag Copy the env template and add your API keys: macOS / Linux: cp .env.example .env Windows Command Prompt : copy .env.example .env Windows PowerShell : Copy-Item .env.example .env Open .env and fill in GROQ API KEY and TAVILY API KEY . 1. Install the Python https://marketplace.visualstudio.com/items?itemName=ms-python.python and Jupyter https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter extensions in VS Code, if you don't already have them. 2. Open the project folder in VS Code File Open Folder... . 3. Open any of the four .ipynb files. 4. In the top-right of the notebook, click Select Kernel and choose the agentic-rag kernel you registered above it may show as .venv Python 3.11 — pick the one whose path points at this project's .venv . 5. Run the cells top to bottom with the ▶ buttons, or Run All . That's the whole setup — no manually creating a virtualenv, no separate pip install -r requirements.txt step, and uv.lock means everyone who runs uv sync gets the exact same dependency versions you tested with. Kernel is selected per notebook, not per project. VS Code remembers a separate kernel choice for each .ipynb file, so it's easy to open a second notebook and have it silently fall back to your global Python install instead of this project's .venv — especially if the .venv kernel hasn't been used in that notebook before. If one notebook runs fine but another throws import errors for packages this project clearly installs e.g. chromadb , langchain chroma , that's the first thing to check: open the kernel picker for the failing notebook and confirm the path points into this project's .venv , not AppData\Local\Programs\Python\... or any other system/global install. 1 Agentic RAG.ipynb scrapes the LangGraph and LangChain documentation into two separate Chroma vector stores, wraps each as a retriever tool, and gives both tools to a tool-calling agent. The agent decides per question whether to call a tool, which one, and whether the retrieved documents are good enough to answer from or need a rewritten query. 2 Corrective RAG.ipynb indexes a small set of blog posts on AI agents into one vector store. Every question always retrieves from it; a grading step then checks each retrieved chunk for relevance. If nothing relevant comes back, the question is rewritten for web search and Tavily fills the gap before the answer is generated. 3 Adaptive RAG.ipynb builds on the same idea but adds a router at the very start should this question go to the vectorstore or straight to the web? and two more graders at the very end, checking that the generated answer is actually grounded in the retrieved documents and actually answers the question — looping back to retry if either check fails. 4 Human in the Loop RAG.ipynb takes the retrieve/grade/generate/self-check machinery from the Adaptive RAG notebook and puts a person in charge of the two decisions that used to be automatic: - After retrieval, each document is graded for relevance as before, but the grade is shown to a human as a suggestion only — the human decides whether to proceed to generation or reject and have the question rewritten and re-retrieved. - After generation, the two self-checks grounded-in-documents, addresses-the-question are shown for reference, but the human has the final call: approve, ask for a revision with their own feedback, or send it back to retrieval. Mechanically, this runs on LangGraph's current recommended HITL pattern: interrupt payload called inside a node pauses the graph and surfaces payload to whoever is running it; resuming happens with graph.stream Command resume=... , config , and a MemorySaver checkpointer persists the graph's state while it's paused. All branching still goes through plain add conditional edges rather than Command goto=... , so every routing decision lives in one place. This notebook only needs GROQ API KEY — it doesn't call Tavily. - Embeddings run locally via sentence-transformers BAAI/bge-m3 — no embedding API key needed. - Vector data is written to a local Chroma store at runtime and is not committed to the repo chroma/ is gitignored . - These notebooks scrape live documentation pages and blog posts at run time, so results will vary slightly as those pages change. 1 Agentic RAG.ipynb is a free demo — use it, share it, redistribute it. The other three notebooks are a paid, personal-use resource — see LICENSE.md . In short: use them, learn from them, build on them, but don't resell or redistribute the notebooks themselves. Get the full bundle here: Agentic RAG — Four Working Patterns with LangGraph https://chandula7.gumroad.com/l/Advanced RAG LangGraph Patterns